Spin-Based Neuron Model with Domain Wall Magnets as Synapse

We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and domain wall magnets for compact, programmable synapses. The spin based neuron-synapse units operate locally at ultra low supply voltage of 30mV resulting in low computation power. CMOS based inter-neuron communication is employed to realize network-level functionality. We corroborate circuit operation with physics based models developed for the spin devices. Simulation results for character recognition as a benchmark application shows 95% lower power consumption as compared to 45nm CMOS design

Low energy digital circuits in advanced nanometer technologies

The demand for portable devices and the continuing trend towards the Internet ofThings (IoT) have made of energy consumption one of the main concerns in the industry and researchers. The most efficient way of reducing the energy consump-tion of digital circuits is decreasing the supply voltage (Vdd) since the dynamicenergy quadratically depends onVdd. Several works have shown that an optimumsupply voltage exists that minimizes the energy consumption of digital circuits. This optimum supply voltage is usually around 200 mV and 400 mV dependingon the circuit and technology used. To obtain these low supply voltages, on-chipdc-dc converters with high efficiency are needed.This thesis focuses on the study of subthreshold digital systems in advancednanometer technologies. These systems usually can be divided into a Power Man-agement Unit (PMU) and a digital circuit operating at the subthreshold regime.In particular, while considering the PMU, one of the key circuits is the dc-dcconverter. This block converts the voltage from the power source (battery, supercapacitor or wireless power transfer link) to a voltage between 200 mV and 400mV in order to power the digital circuit. In this thesis, we developed two chargerecycling techniques in order to improve the efficiency of switched capacitors dc-dcconverters. The first one is based on a technique used in adiabatic circuits calledstepwise charging. This technique was used in circuits and applications wherethe switching consumption of a big capacitance is very important. We analyzedthe possibility of using this technique in switched capacitor dc-dc converters withintegrated capacitors. We showed through measurements that a 29% reductionin the gate drive losses can be obtained with this technique. The second one isa simplification of stepwise charging which can be applied in some architecturesof switched capacitors dc-dc converters. We also fabricated and tested a dc-dcconverter with this technique and obtained a 25% energy reduction in the drivingof the switches that implement the converter.Furthermore, we studied the digital circuit working in the subthreshold regime,in particular, operating at the minimum energy point. We studied different modelsfor circuits working in these conditions and improved them by considering thedifferences between the NMOS and PMOS transistors. We obtained an optimumNMOS/PMOS leakage current imbalance that minimizes the total leakage energy per operation. This optimum depends on the architecture of the digital circuitand the input data. However, we also showed that important energy reductionscan be obtained by operating at a mean optimum imbalance. We proposed two techniques to achieve the optimum imbalance. We used aFully Depleted Silicon on Insulator (FD-SOI) 28 nm technology for most of the simulations, but we also show that these techniques can be applied in traditionalbulk CMOS technologies. The first one consists in using the back plane voltage of the transistors (or bulk voltage in traditional CMOS) to adjust independently theleakage current of the NMOS and PMOS transistor to work under the optimum NMOS/PMOS leakage current imbalance. We called this approach the OptimumBack Plane Biasing (OBB). A second technique consists of using the length of the transistors to adjust this leakage current imbalance. In the subthreshold regimeand in advanced nanometer technologies a moderate increase in the length has little impact in the output capacitance of the gates and thus in the dynamic energy.We called this approach an Asymmetric Length Biasing (ALB). Finally, we use these techniques in some basic circuits such as adders. We show that around 50% energy reduction can be obtained, in a wide range of frequency while working near the minimum energy point and using these techniques. The main contributions of this thesis are: • Analysis of the stepwise charging technique in small capacitances. •Implementation of stepwise charging technique as a charge recycling tech-nique for efficiency improvement in switched capacitor dc-dc converters. • Development of a charge sharing technique for efficiency improvement inswitched capacitor dc-dc converters. • Analysis of minimum operating voltage of digital circuits due to intrinsicnoise and the impact of technology scaling in this minimum. • Improvement in the modeling of the minimum energy point while considering NMOS and PMOS transistors difference. • Demonstration of the existence of an optimum leakage current imbalance be-tween the NMOS and PMOS transistors that minimizes energy consumptionin the subthreshold regiion. • Development of a back plane (bulk) voltage strategy for working in this optimum.• Development of a sizing strategy for working in the aforementioned optimum. • Analysis of the impact of architecture and input data on the optimum im-balance. The thesis is based on the publications [1–8]. During the Ph.D. program, other publications were generated [9–16] that are partially related with the thesis butwere not included in it.La constante demanda de dispositivos portables y los avances hacia la Internet de las Cosas han hecho del consumo de energía uno de los mayores desafíos y preocupación en la industria y la academia. La forma más eficiente de reducir el consumo de energía de los circuitos digitales es reduciendo su voltaje de alimentación ya que la energía dinámica depende de manera cuadrática con dicho voltaje. Varios trabajos demostraron que existe un voltaje de alimentación óptimo, que minimiza la energía consumida para realizar cierta operación en un circuito digital, llamado punto de mínima energía. Este óptimo voltaje se encuentra usualmente entre 200 mV y 400 mV dependiendo del circuito y de la tecnología utilizada. Para obtener estos voltajes de alimentación de la fuente de energía, se necesitan conversores dc-dc integrados con alta eficiencia. Esta tesis se concentra en el estudio de sistemas digitales trabajando en la región sub umbral diseñados en tecnologías nanométricas avanzadas (28 nm). Estos sistemas se pueden dividir usualmente en dos bloques, uno llamado bloque de manejo de potencia, y el segundo, el circuito digital operando en la region sub umbral. En particular, en lo que corresponde al bloque de manejo de potencia, el circuito más crítico es en general el conversor dc-dc. Este circuito convierte el voltaje de una batería (o super capacitor o enlace de transferencia inalámbrica de energía o unidad de cosechado de energía) en un voltaje entre 200 mV y 400 mV para alimentar el circuito digital en su voltaje óptimo. En esta tesis desarrollamos dos técnicas que, mediante el reciclado de carga, mejoran la eficiencia de los conversores dc-dc a capacitores conmutados. La primera es basada en una técnica utilizada en circuitos adiabáticos que se llama carga gradual o a pasos. Esta técnica se ha utilizado en circuitos y aplicaciones en donde el consumo por la carga y descarga de una capacidad grande es dominante. Nosotros analizamos la posibilidad de utilizar esta técnica en conversores dc-dc a capacitores conmutados con capacitores integrados. Se demostró a través de medidas que se puede reducir en un 29% el consumo debido al encendido y apagado de las llaves que implementan el conversor dc-dc. La segunda técnica, es una simplificación de la primera, la cual puede ser aplicada en ciertas arquitecturas de conversores dc-dc a capacitores conmutados. También se fabricó y midió un conversor con esta técnica y se obtuvo una reducción del 25% en la energía consumida por el manejo de las llaves del conversor. Por otro lado, estudiamos los circuitos digitales operando en la región sub umbral y en particular cerca del punto de mínima energía. Estudiamos diferentes modelos para circuitos operando en estas condiciones y los mejoramos considerando las diferencias entre los transistores NMOS y PMOS. Mediante este modelo demostramos que existe un óptimo en la relación entre las corrientes de fuga de ambos transistores que minimiza la energía de fuga consumida por operación. Este óptimo depende de la arquitectura del circuito digital y ademas de los datos de entrada del circuito. Sin embargo, demostramos que se puede reducir el consumo de manera considerable al operar en un óptimo promedio. Propusimos dos técnicas para alcanzar la relación óptima. Utilizamos una tecnología FD-SOI de 28nm para la mayoría de las simulaciones, pero también mostramos que estas técnicas pueden ser utilizadas en tecnologías bulk convencionales. La primer técnica, consiste en utilizar el voltaje de la puerta trasera (o sustrato en CMOS convencional) para ajustar de manera independiente las corrientes del NMOS y PMOS para que el circuito trabaje en el óptimo de la relación de corrientes. Esta técnica la llamamos polarización de voltaje de puerta trasera óptimo. La segunda técnica, consiste en utilizar los largos de los transistores para ajustar las corrientes de fugas de cada transistor y obtener la relación óptima. Trabajando en la región sub umbral y en tecnologías avanzadas, incrementar moderadamente el largo del transistor tiene poco impacto en la energía dinámica y es por eso que se puede utilizar. Finalmente, utilizamos estas técnicas en circuitos básicos como sumadores y mostramos que se puede obtener una reducción de la energía consumida de aproximadamente 50%, en un amplio rango de frecuencias, mientras estos circuitos trabajan cerca del punto de energía mínima. Las principales contribuciones de la tesis son: • Análisis de la técnica de carga gradual o a pasos en capacidades pequeñas. • Implementación de la técnica de carga gradual para la mejora de eficiencia de conversores dc-dc a capacitores conmutados. • Simplificación de la técnica de carga gradual para mejora de la eficiencia en algunas arquitecturas de conversores dc-dc de capacitores conmutados. • Análisis del mínimo voltaje de operación en circuitos digitales debido al ruido intrínseco del dispositivo y el impacto del escalado de las tecnologías en el mismo. • Mejoras en el modelado del punto de energía mínima de operación de un circuito digital en el cual se consideran las diferencias entre el transistor PMOS y NMOS. • Demostración de la existencia de un óptimo en la relación entre las corrientes de fuga entre el NMOS y PMOS que minimiza la energía de fugas consumida en la región sub umbral. • Desarrollo de una estrategia de polarización del voltaje de puerta trasera para que el circuito digital trabaje en el óptimo antes mencionado. • Desarrollo de una estrategia para el dimensionado de los transistores que componen las compuertas digitales que permite al circuito digital operar en el óptimo antes mencionado. • Análisis del impacto de la arquitectura del circuito y de los datos de entrada del mismo en el óptimo antes mencionado

Low-Power Circuits for Brain–Machine Interfaces

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson’s disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode- recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented

A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 μm SOI CMOS

In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-μm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 μm wide, 10 mm long, 20 μm thick), achieving a crosstalk of −64.4 dB. The probe base (5 × 9 mm2) implements dual-band recording and a 1

Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine

Deep neural networks have achieved impressive results in computer vision and machine learning. Unfortunately, state-of-the-art networks are extremely compute and memory intensive which makes them unsuitable for mW-devices such as IoT end-nodes. Aggressive quantization of these networks dramatically reduces the computation and memory footprint. Binary-weight neural networks (BWNs) follow this trend, pushing weight quantization to the limit. Hardware accelerators for BWNs presented up to now have focused on core efficiency, disregarding I/O bandwidth and system-level efficiency that are crucial for deployment of accelerators in ultra-low power devices. We present Hyperdrive: a BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel binary-weight streaming approach, which can be used for arbitrarily sized convolutional neural network architecture and input resolution by exploiting the natural scalability of the compute units both at chip-level and system-level by arranging Hyperdrive chips systolically in a 2D mesh while processing the entire feature map together in parallel. Hyperdrive achieves 4.3 TOp/s/W system-level efficiency (i.e., including I/Os)---3.1x higher than state-of-the-art BWN accelerators, even if its core uses resource-intensive FP16 arithmetic for increased robustness

Characterization of 28 nm FDSOI MOS and application to the design of a low-power 2.4 GHz LNA

IoT is expected to connect billions of devices all over world in the next years, and in a near future, it is expected to use LR-WPAN in a wide variety of applications. Not all the devices will require of high performance but will require of low power hungry systems since most of them will be powered with a battery. Conventional CMOS technologies cannot cover these needs even scaling it to very small regimes, which appear other problems. Hence, new technologies are emerging to cover the needs of this devices. One promising technology is the UTBB FDSOI, which achieves good performance with very good energy efficiency. This project characterizes this technology to obtain a set of parameters of interest for analog/RF design. Finally, with the help of a low-power design methodology (gm/Id approach), a design of an ULP ULV LNA is performed to check the suitability of this technology for IoT